code: def test_task_classification_input(
      random_cls_dataset, linear_cls_model
  ):
      """
      Classification task explainer works when `Y_train` is `None` or a `np.ndarray`.
      """
      backend, model, loss_fn, target_fn = linear_cls_model
      (X_train, Y_train), (_, _) = random_cls_dataset
  
      classification_explainer = GradientSimilarity(
          predictor=model,
          loss_fn=loss_fn,
          sim_fn="grad_dot",
          task="classification",
          precompute_grads=False,
          backend=backend,
      )
  
      # classification similarity method y value can be none or a ndarray
      classification_explainer.fit(X_train=X_train, Y_train=Y_train)
      classification_explainer.explain(X_train[0:1])
      classification_explainer.explain(X_train[0:1], Y_train[0:1])
  
file_path: alibi/alibi/explainers/tests/test_simiarlity/test_grad_methods_unit.py
imports: import pytest
  from alibi.explainers.tests.test_simiarlity.conftest import get_flattened_model_parameters
  from alibi.explainers.similarity.grad import GradientSimilarity, Task
  from alibi.utils import _get_options_string
label_desc: Use image node_id 5 for calling a global function with example usage: test_task_classification_input(random_cls_dataset, linear_cls_model) without return types